RegML 2018
Regularization Methods for Machine Learning

Course at a Glance

This year (18-22 June 2018) RegML will be hold in Genova and co-organized by SIMULA

Understanding how intelligence works and how it can be emulated in machines is an age old dream and arguably one of the biggest challenges in modern science. Learning, with its principles and computational implementations, is at the very core of this endeavor. Recently, for the first time, we have been able to develop artificial intelligence systems able to solve complex tasks considered out of reach for decades. Modern cameras recognize faces, and smart phones voice commands, cars can see and detect pedestrians and ATM machines automatically read checks. In most cases at the root of these success stories there are machine learning algorithms, that is softwares that are trained rather than programmed to solve a task. Among the variety of approaches to modern computational learning, we focus on regularization techniques, that are key to high- dimensional learning. Regularization methods allow to treat in a unified way a huge class of diverse approaches, while providing tools to design new ones. Starting from classical notions of smoothness, shrinkage and margin, the course will cover state of the art techniques based on the concepts of geometry (aka manifold learning), sparsity and a variety of algorithms for supervised learning, feature selection, structured prediction, multitask learning and model selection. Practical applications for high dimensional problems, in particular in computational vision, will be discussed. The classes will focus on algorithmic and methodological aspects, while trying to give an idea of the underlying theoretical underpinnings. Practical laboratory sessions will give the opportunity to have hands on experience.


RegML is a 22 hours advanced machine learning course including theory classes and practical laboratory sessions. The course covers foundations as well as recent advances in Machine Learning with emphasis on high dimensional data and a core set techniques, namely regularization methods. In many respect the course is compressed version of the 9.520 course at MIT".


The course started in 2008 has seen an increasing national and international attendance over the years with a peak of over 90 participants in 2014.


NOTE: the course has no registration fee, but participants need to take care of their travel and accommodation needs -- see below for a list of hotels.


Notification of acceptance: To be announced.

Related courses:

Basic Info


Dates

The school will be from 18th to 22nd June 2018.


Venue

Classes will take place at the Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) of the University of Genova in Via Dodecaneso 35, 16146 Genova. See here for directions and travelling information


Genova

Genova is in the region of Liguria in the Italian Riviera (see here or here for some nice pics and a video).


Accomodations

Here you can find a list of hotels near the department (~ 20' walk) or in the city centre (~20' by bus).


Lunch

Here is a list of places where you can go for lunch. And here is a link to the online map.


Instructors

Lorenzo Rosasco

Università di Genova
Istituto Italiano di Tecnologia
Massachusetts Institute of Technology

lorenzo (dot) rosasco (at) unige (dot) it

Teaching Assistants

Luigi Carratino

Università di Genova

luigi (dot) carratino (at) dibris (dot) unige (dot) it

Nicole Mücke

Istituto Italiano di Tecnologia

nicole (dot) muecke (at) iit (dot) it

Daniele Calandriello

Istituto Italiano di Tecnologia

daniele (dot) calandriello (at) iit (dot) it



Workshop

Invited Speakers

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Syllabus

CLASS DAY TIME SUBJECT FILES
1Introduction to Machine LearningLect 1
2Local Methods and Model SelectionLect 2
3Laboratory 1: Local Methods for Classification Lab 1
4Tikhonov Regularization and KernelsLect 3
5Laboratory 2: Binary classification and model selectionLab 2
6Early Stopping and Spectral RegularizationLect 4
7Regularization for Multi-task LearningLect 5
8Laboratory 3: Spectral filters and multi-class classificationLab 3
9Sparsity Based RegularizationLect 6
10Structured SparsityLect 7
11Laboratory 4: Sparsity-based learningLab 4
- -Workshop

The deadline for applications is May 1

Organizers

Lorenzo Rosasco

Università di Genova
Istituto Italiano di Tecnologia
Massachusetts Institute of Technology

lorenzo (dot) rosasco (at) unige (dot) it

Luigi Carratino

Università di Genova

luigi (dot) carratino (at) dibris (dot) unige (dot) it

Nicole Mücke

Istituto Italiano di Tecnologia

nicole (dot) muecke (at) iit (dot) it

Daniele Calandriello

Istituto Italiano di Tecnologia

daniele (dot) calandriello (at) iit (dot) it